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Instruments, Agents, and Artificial Intelligence: Novel Epistemic Categories of Reliability

Duede, Eamon (2022) Instruments, Agents, and Artificial Intelligence: Novel Epistemic Categories of Reliability. [Preprint]

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Abstract

Deep learning (DL) has become increasingly central to science, primarily due to its capacity to quickly, efficiently, and accurately predict and classify phenomena of scientific interest. This paper seeks to understand the principles that underwrite scientists’ epistemic entitlement to rely on DL in the first place and argues that these principles are philosophically novel. The question of this paper is not whether scientists can be justified in trusting in the reliability of DL. While today's artificial intelligence exhibits characteristics common to both scientific instruments and scientific experts, this paper argues that the familiar epistemic categories that justify belief in the reliability of instruments and experts are distinct, and that belief in the reliability of DL cannot be reduced to either. Understanding what can justify belief in AI reliability represents an occasion and opportunity for exciting, new philosophy of science.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Duede, Eamoneduede@uchicago.edu0000-0002-3592-0478
Additional Information: This paper is forthcoming in a special issue of Synthese titled "Philosophy of Science in Light of Artificial Intelligence".
Keywords: deep learning, scientific knowledge, models, trust, artificial intelligence
Subjects: Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Technology
Depositing User: Eamon Duede
Date Deposited: 06 Nov 2022 15:48
Last Modified: 06 Nov 2022 15:48
Item ID: 21352
Subjects: Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Technology
Date: 6 November 2022
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/21352

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